Evidence map›Paper›PMID 24297757›Full record

ArticleHuman genetics2014

Exploring the genetic architecture of alcohol dependence in African-Americans via analysis of a genomewide set of common variants.

Can Yang, Cong Li, Henry R Kranzler, Lindsay A Farrer, Hongyu Zhao, Joel Gelernter

Abstract read
In one paragraph

Article in Human genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Review
  9. Genome-wide association study of copy number variations (CNVs) with opioid dependence.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2015
    Article
  10. Article
  11. Review
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Can YangDepartment of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, 06520, USA, can.yang@yale.edu.
Cong Li
Henry R Kranzler
Lindsay A Farrer
Hongyu Zhao
Joel Gelernter

Funding

Yale Diabetes Research CenterP30DK045735 · NIDDK · YALE UNIVERSITY · PI Richard A Flavell · 1993 to 2026
$44.0M
Translational Technologies CoreP50AA012870 · NIAAA · YALE UNIVERSITY · PI Matthew J Girgenti · 2001 to 2026
$43.7M
Yale University Clinical and Translational Science Award ProgramUL1TR000142 · NCATS · YALE UNIVERSITY · PI SHERWIN, ROBERT S · 2012 to 2015
$31.8M
Deep sequencing studies for cannabis and stimulant dependenceR01DA030976 · NIDA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI EHLERS, CINDY L, GELERNTER, JOEL · 2010 to 2014
$16.5M
Sex-specific analysis of opioid dependence GWAS and beyondR01DA012690 · NIDA · YALE UNIVERSITY · PI GELERNTER, JOEL · 2000 to 2017
$14.0M
GENETICS OF COCAINE DEPENDENCER01DA012849 · NIDA · YALE UNIVERSITY · PI GELERNTER, JOEL · 1999 to 2010
$11.5M
Linkage Disequilibrium Studies of Alcohol DependenceR01AA011330 · NIAAA · YALE UNIVERSITY · PI GELERNTER, JOEL · 1997 to 2013
$6.4M
Genomewide Association Study of Cocaine Dependence in two PopulationsRC2DA028909 · NIDA · YALE UNIVERSITY · PI GELERNTER, JOEL · 2009 to 2010
$4.2M
STATISTICAL METHODS TO MAP GENES FOR COMPLEX TRAITSR01GM059507 · NIGMS · YALE UNIVERSITY · PI ZHAO, HONGYU · 1999 to 2013
$3.6M
Genetics of Cocaine DependenceR01DA018432 · NIDA · UNIVERSITY OF CONNECTICUT SCH OF MED/DNT · PI KRANZLER, HENRY RICHARD · 2006 to 2010
$3.5M
Genetics of Alcohol Dependence in African-AmericansR01AA017535 · NIAAA · YALE UNIVERSITY · PI GELERNTER, JOEL · 2009 to 2013
$3.0M
GENOTYPING FOR LOCATING HUMAN DISEASE GENESN01HG065403 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI VALLE, DAVID L · 1996 to 2000
$1.7M
NCATS NIH HHS UL1 TR000142NHGRI NIH HHS N01 HG065403NIAAA NIH HHS P50 AA012870NIAAA NIH HHS P50 AA12870NIAAA NIH HHS R01 AA011330NIAAA NIH HHS R01 AA017535NIAAA NIH HHS R01 AA11330NIDA NIH HHS R01 DA012690NIDA NIH HHS R01 DA012849NIDA NIH HHS R01 DA018432NIDA NIH HHS R01 DA030976NIDA NIH HHS R01 DA12690NIDA NIH HHS R01 DA12849NIDA NIH HHS R01 DA18432NIDA NIH HHS RC2 DA028909NIDDK NIH HHS P30 DK045735NIGMS NIH HHS R01 GM059507NIGMS NIH HHS R01-GM59507
6 · The paper itself

Abstract

Alcohol dependence (AD) is a complex psychiatric disorder that affects about 12.5 % of US adults. Genetic factors play a major role in the development of AD. We conducted a genomewide association study in 2,875 African-Americans including 1,719 AD cases and 1,156 controls. We used the Illumina Omni 1-Quad microarray, which yielded 769,498 single-nucleotide polymorphisms (SNPs) after quality control. To explore the genetic architecture of AD, we estimated the variance that could be explained by all SNPs and subsets of SNPs using two different approaches to genome partitioning. We found that 23.9 % (s.e. 9.3 %) of the phenotypic variance could be explained by using all of the common SNPs on the array. We also found a significant linear relationship between the proportion of the top SNPs used and the phenotypic variance explained by them. Based on genome partitioning of common variants, we also observed a significant linear relationship between the variance explained by a chromosome and its length. Chromosome 4, known to contain several AD risk genes, accounted for excess risk in proportion to its length. By functional partitioning, we found that the genetic variants within 20 kb of genes explained 17.5 % (s.e. 11.4 %) of the phenotypic variance. Our findings are consistent with the generally accepted view that AD is a highly polygenic trait, i.e., the genetic risk in AD appears to be conferred by multiple variants, each of which may have a small or moderate effect.

Indexed as

Genome-Wide Association StudyAlcoholismBlack or African AmericanBlack PeopleHumansPolymorphism, Single Nucleotide

Identifiers

PMID24297757
PMCPMC3988209

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.